- Multimodal learning and representation alignment
- Image and object detection and analysis
- Time series forecasting and sequential modeling
- Domain generalization and robustness under distribution shift
- Learning under noise (label noise, data corruption, uncertainty)
- Self-supervised and unsupervised representation learning
- Neural collapse and geometry of learned representations
- Medical imaging analysis (e.g., brain tumor detection)
- Linear algebra and matrix analysis
- Probability theory and mathematical statistics
- Optimization (convex and non-convex)
- Information theory
- Approximation algorithms and combinatorial optimization
- Learning theory (PAC learning, VC dimension)
- Differential privacy and privacy-preserving learning
- Secure data analysis and robustness against inference attacks
- Cryptographic primitives and threat modeling (foundational)
- Adversarial machine learning and security of ML systems
📫 Contact: sukanyasingh770@gmail.com